Paper Title
ETL for IoT Data: Integrating Sensor Data into Data Warehouses
Article Identifiers
Authors
Dhamotharan Seenivasan
Keywords
ETL (Extract, Transform, Load), IoT (Internet of Things), Sensor Data, Data Warehouses, Data Integration, Data Transformation, Big Data
Abstract
Today, various sensors are present in the innovations of IoT technology, which results in a huge amount of generated data. This information has to be incorporated into data warehouses; therefore, it needs to go through sound ETL (Extract, Transform, and Load) procedures. The methodologies and technologies employed for the IoT sensor data integration into the data warehouses are hence described in this paper, the challenges involved with recommendations. Some of the issues relevant to big data contexts are data collection and pre-processing from different and heterogeneous IoT devices, as well as properly loading, normalizing and transforming the IoT data into a data warehouse. Real-time data processing and analytics are explained, and examples, as well as the practical application of real-time analytics, are also presented. The objective of this research is to give a clear picture and understand the role played by the ETL process when dealing with IoT data a how it makes it easy to carry out advanced analysis and making of future decisions.
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How To Cite (APA)
Dhamotharan Seenivasan (October-2022). ETL for IoT Data: Integrating Sensor Data into Data Warehouses. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(10), 482-493. https://ijnrd.org/papers/IJNRD2210185.pdf
Issue
Volume 7 Issue 10, October-2022
Pages : 482-493
Other Publication Details
Paper Reg. ID: IJNRD_226331
Published Paper Id: IJNRD2210185
Downloads: 000121993
Research Area: Engineering
Country: -, -, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2210185.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2210185
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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